An Information-Theoretic Analysis of Thompson Sampling

21 March 2014

Papers citing "An Information-Theoretic Analysis of Thompson Sampling"

31 / 81 papers shown

Title
Is Pessimism Provably Efficient for Offline RL? Ying Jin Zhuoran Yang Zhaoran Wang OffRL 27 350 0 30 Dec 2020
Asymptotic Convergence of Thompson Sampling Cem Kalkanli Ayfer Özgür 8 5 0 08 Nov 2020
Adaptive Combinatorial Allocation Maximilian Kasy A. Teytelboym 10 3 0 04 Nov 2020
Randomized Value Functions via Posterior State-Abstraction Sampling Dilip Arumugam Benjamin Van Roy OffRL 33 7 0 05 Oct 2020
On Information Gain and Regret Bounds in Gaussian Process Bandits Sattar Vakili Kia Khezeli Victor Picheny GP 29 128 0 15 Sep 2020
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation Jackie Baek Vivek F. Farias 45 9 0 11 Jun 2020
Sequential Batch Learning in Finite-Action Linear Contextual Bandits Yanjun Han Zhengqing Zhou Zhengyuan Zhou Jose H. Blanchet Peter Glynn Yinyu Ye OffRL 9 71 0 14 Apr 2020
Effective Diversity in Population Based Reinforcement Learning Jack Parker-Holder Aldo Pacchiano K. Choromanski Stephen J. Roberts 22 158 0 03 Feb 2020
Offline Contextual Bayesian Optimization for Nuclear Fusion Youngseog Chung I. Char Willie Neiswanger Kirthevasan Kandasamy Oakleigh Nelson M. Boyer E. Kolemen J. Schneider OffRL AI4CE 36 13 0 06 Jan 2020
Model Inversion Networks for Model-Based Optimization Aviral Kumar Sergey Levine OffRL 40 94 0 31 Dec 2019
Neural Contextual Bandits with UCB-based Exploration Dongruo Zhou Lihong Li Quanquan Gu 38 15 0 11 Nov 2019
Safe Linear Thompson Sampling with Side Information Ahmadreza Moradipari Sanae Amani M. Alizadeh Christos Thrampoulidis 27 42 0 06 Nov 2019
Exploration by Optimisation in Partial Monitoring Tor Lattimore Csaba Szepesvári 33 38 0 12 Jul 2019
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio Julian Zimmert Tor Lattimore 30 34 0 28 May 2019
Feedback graph regret bounds for Thompson Sampling and UCB Thodoris Lykouris Éva Tardos Drishti Wali 19 29 0 23 May 2019
Functional Variational Bayesian Neural Networks Shengyang Sun Guodong Zhang Jiaxin Shi Roger C. Grosse BDL 22 235 0 14 Mar 2019
An Information-Theoretic Analysis for Thompson Sampling with Many Actions Shi Dong Benjamin Van Roy 14 49 0 30 May 2018
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming Kirthevasan Kandasamy Willie Neiswanger Reed Zhang A. Krishnamurthy J. Schneider Barnabás Póczós 22 5 0 25 May 2018
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits Bianca Dumitrascu Karen Feng Barbara E. Engelhardt 19 41 0 18 May 2018
Thompson Sampling for Combinatorial Semi-Bandits Siwei Wang Wei Chen 22 125 0 13 Mar 2018
Online Learning: A Comprehensive Survey Guosheng Lin Doyen Sahoo Jing Lu P. Zhao OffRL 31 636 0 08 Feb 2018
Information Directed Sampling and Bandits with Heteroscedastic Noise Johannes Kirschner Andreas Krause 24 122 0 29 Jan 2018
Taming Non-stationary Bandits: A Bayesian Approach Vishnu Raj Sheetal Kalyani 38 76 0 31 Jul 2017
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization Jonathan Scarlett Ilija Bogunovic V. Cevher 33 99 0 31 May 2017
Human Interaction with Recommendation Systems S. Schmit C. Riquelme 24 51 0 01 Mar 2017
Efficient simulation of high dimensional Gaussian vectors N. Kahalé 14 4 0 28 Feb 2017
Thompson Sampling For Stochastic Bandits with Graph Feedback Aristide C. Y. Tossou Christos Dimitrakakis Devdatt Dubhashi 19 28 0 16 Jan 2017
Corralling a Band of Bandit Algorithms Alekh Agarwal Haipeng Luo Behnam Neyshabur Robert Schapire 30 154 0 19 Dec 2016
Double Thompson Sampling for Dueling Bandits Huasen Wu Xin Liu 22 87 0 25 Apr 2016
Global Bandits Onur Atan Cem Tekin Mihaela van der Schaar 34 16 0 29 Mar 2015
Efficient Learning in Large-Scale Combinatorial Semi-Bandits Zheng Wen Branislav Kveton Azin Ashkan OffRL 59 96 0 28 Jun 2014